Abstract
AbstractJouyban‐Acree/van't Hoff model was often used to correlate solubility data, which was related to temperature, and the mole fraction of each solvent in the mixture of two solvents. In this work, the partial molar volume of each component in the mixture of two solvents was first time introduced as a modification to the Jouyban‐Acree/van't Hoff model. Furthermore, machine learning and artificial intelligence (AI) technology based on temperature, mole fraction, and partial molar volume of each solvent were employed to improve the accuracy of the solubility estimation. The models were evaluated in terms of their ability to mathematically correlate solute solubility in binary solvents. An average root mean square deviation (RMSD) is used to measure the deviation between the calculated values and the experimental values. With partial molar volume as a modified parameter of the Jouyban‐Acree/van't Hoff model, the overall RMSD of all the correlations of solubility improved.
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